F1 Layout

Map indicates the layout of the F1 generation resulting from a cross between EC201 and EC103 parents. Column 1 is approximately lengthways facing north.

“Map of F1 Trees”

“Map of F1 Trees”

Height Diagram

Diagram indicates the areas of leaf collection regarding height. Each Tree had 10 leaves collected, 3 from Low, 4 from Mid and 3 from High. The first leaf sampled was measured twice for replication comparison.

“Diagram of leaf collection levels”

“Diagram of leaf collection levels”

Import and Arrange Data

“Full.xlsx” contains measurement information from sampling with the Dualex (https://www.force-a.com/en/capteurs-optiques-optical-sensors/dualex-scientific-chlorophyll-meter/), including;

It also contains information about the block position, the leaf height information, and presense or absence of flowering

Sheet “Dup” contains only the samples that were replicated.

# Import Data Measures
Data <-read.xlsx("Full.xlsx", sheetName ="Full")
head(Data)
##   Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 1              4         CG     0      0   0    20       OG      CG    N
## 2              4         CG     0      0   0    20       OG      CG    N
## 3              4         CG     0      0   0    20       OG      CG    N
## 4              4         CG     0      0   0    20       OG      CG    N
## 5              4         CG     0      0   0    20       OG      CG    N
## 6              4         CG     0      0   0    20       OG      CG    N
##   measure Height Flower    Chl  Flav  Anth   NBI
## 1       9      H      Y 28.220 2.418 0.205 11.67
## 2       3      L      Y 27.958 2.298 0.691 12.17
## 3      11      H      Y 33.727 2.458 0.527 13.72
## 4       4      L      Y 25.938 1.758 0.172 14.76
## 5      10      H      Y 36.205 2.283 0.270 15.86
## 6       6      M      Y 34.332 2.115 0.150 16.23
Data$Column = as.factor(Data$Column)
Data$Row = as.factor(Data$Row)

# Import Replicate Data
Dup <-read.xlsx("Full.xlsx", sheetName ="Dup")
head(Dup)
##   Collection.Day group Group.ID Tree.ID Rep. measure Height   Chl  Flav
## 1            2.0    23       60   IN4DV   Y1       1      L 1.916 2.363
## 2            1.5     3        1   IN4BT   Y1       1      L 3.124 2.300
## 3            2.0    17       54   IN4DL   Y2       2      L 3.414 1.826
## 4            1.5    32       28   IN4CP   Y2       2      L 4.097 1.943
## 5            2.0     9       46   IN4DC   Y1       1      L 4.909 1.928
## 6            1.5     5        3   IN4BW   Y1       1      L 4.924 1.848
##    Anth  NBI
## 1 0.174 0.81
## 2 0.191 1.36
## 3 0.112 1.87
## 4 0.051 2.11
## 5 0.103 2.55
## 6 0.165 2.66
#Isolate Crimson Glory Outgroup
CG = Data[c(1:11),]

#Isolate East Cape 201 Parent
EC201 = Data[c(12:21),]

#Isolate East Cape 103 Parent
EC103 = Data[c(22:33),]

#Isolate Offspring from the Parental Cross
F1 = Data[c(34:1825),]

Replicate Analysis

Replicates were taken by measuring a single leaf sample from each tree twice, in order to establish consistency and reliability of measurements with the Dualex.

Replicate Data Overview

##                Min.     1st Qu.      Median        Mean     3rd Qu.
## RepAnth  0.00100000  0.06500000  0.09850000  0.09799367  0.12525000
## RepChl   1.91600000 25.82475000 36.02700000 35.76269937 46.61000000
## RepFlav  1.05600000  1.76775000  1.99400000  1.96720570  2.19725000
## RepNBI   0.81000000 12.70250000 18.19000000 18.73398734 24.45750000
##                Max.
## RepAnth  0.25400000
## RepChl  59.67400000
## RepFlav  2.71400000
## RepNBI  43.49000000

Replicate Group means

## # A tibble: 2 x 5
##   Rep.    Anth   Chl  Flav   NBI
##   <fct>  <dbl> <dbl> <dbl> <dbl>
## 1 Y1    0.0983  34.9  1.98  18.2
## 2 Y2    0.0977  36.6  1.95  19.2

Anthocyanin Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorphyll Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen Balance Replicate Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

There is no statistically significant differences between the two groups of measurements, this is a good sign indicative of the accuracy of the Dualex.

ANOVA of Replicates

## Analysis of Variance Table
## 
## Response: Dup$Chl
##            Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep.    1    218  218.33   1.033 0.3102
## Residuals 314  66366  211.36
## Analysis of Variance Table
## 
## Response: Dup$Flav
##            Df  Sum Sq  Mean Sq F value Pr(>F)
## Dup$Rep.    1  0.0521 0.052129  0.6506 0.4205
## Residuals 314 25.1596 0.080126
## Analysis of Variance Table
## 
## Response: Dup$Anth
##            Df  Sum Sq   Mean Sq F value Pr(>F)
## Dup$Rep.    1 0.00003 0.0000304  0.0151 0.9023
## Residuals 314 0.63303 0.0020160
## Analysis of Variance Table
## 
## Response: Dup$NBI
##            Df  Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep.    1    82.5  82.530   1.115 0.2918
## Residuals 314 23241.0  74.016

The absence of statistically significant results indicates that our replicates are likely to be consistent.

Allocation Analysis

Allocation refers to which group measurements were taken from, i.e. A Parental Tree (EC103 or EC201), Outgroup Tree (CG), Parental Offspring (F1)

Allocation Data Overview

##                Min.     1st Qu.      Median        Mean     3rd Qu.
## AllAnth  0.00100000  0.06800000  0.09600000  0.09772877  0.12400000
## AllChl   0.13000000 24.10200000 36.21000000 35.21443342 46.82300000
## AllFlav  1.05600000  1.80600000  1.97800000  1.96440493  2.12800000
## AllNBI   0.07000000 12.14000000 18.64000000 18.34807123 24.44000000
##                Max.
## AllAnth  0.69100000
## AllChl  59.90500000
## AllFlav  2.86100000
## AllNBI  49.17000000

Allocation Group Means

## # A tibble: 4 x 5
##   Allocation   Anth   Chl  Flav   NBI
##   <fct>       <dbl> <dbl> <dbl> <dbl>
## 1 CG         0.258   36.0  2.17  16.7
## 2 EC103      0.0588  52.6  1.60  33.1
## 3 EC201      0.0779  52.6  1.71  31.7
## 4 F1         0.0971  35.0  1.97  18.2

Boxplot comparing mean, median and measurement distributions of Allocations

Allocation ANOVAs

## Analysis of Variance Table
## 
## Response: Data$Chl
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   6742 2247.21  9.9149 1.748e-06 ***
## Residuals       1821 412728  226.65                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: Data$Chl
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   6742 2247.21  9.9149 1.748e-06 ***
## Residuals       1821 412728  226.65                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: Data$Flav
##                   Df  Sum Sq Mean Sq F value    Pr(>F)    
## Data$Allocation    3   2.713 0.90423  14.755 1.708e-09 ***
## Residuals       1821 111.598 0.06128                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: Data$Anth
##                   Df Sum Sq  Mean Sq F value    Pr(>F)    
## Data$Allocation    3 0.3040 0.101343  47.474 < 2.2e-16 ***
## Residuals       1821 3.8873 0.002135                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

F1 Samples (Approx 3 years) appear more similar to that of the Crimson Glory plant than either/both parents - this is possibly due to age effects as CG is likely more similar in this regard being shorter (No age confirmed). ANOVAs incidate there is significant differences between the allocations - this is to be expected.

Parent Tree Analysis

Parent Tree Means

## # A tibble: 2 x 5
##   Tree.ID   Anth   Chl  Flav   NBI
##   <fct>    <dbl> <dbl> <dbl> <dbl>
## 1 EC103   0.0588  52.6  1.60  33.1
## 2 EC201   0.0779  52.6  1.71  31.7

Parent Tree Data Overview

##              Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
## ParAnth  0.004000  0.048250  0.058000  0.067500  0.086750  0.156000
## ParChl  30.722000 50.356750 55.739000 52.598909 57.794750 59.757000
## ParFlav  1.293000  1.475000  1.594000  1.650273  1.821000  2.138000
## ParNBI  14.370000 29.872500 34.025000 32.481818 35.892500 44.880000

Anthocyanin Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorophyll Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen Parent Plots

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Parent t.tests

## 
##  Welch Two Sample t-test
## 
## data:  Parent$Anth by Parent$Tree.ID
## t = -1.2283, df = 19.107, p-value = 0.2342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05154327  0.01340994
## sample estimates:
## mean in group EC103 mean in group EC201 
##          0.05883333          0.07790000
## 
##  Welch Two Sample t-test
## 
## data:  Parent$Chl by Parent$Tree.ID
## t = -0.003842, df = 17.804, p-value = 0.997
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.830954  7.802387
## sample estimates:
## mean in group EC103 mean in group EC201 
##            52.59242            52.60670
## 
##  Welch Two Sample t-test
## 
## data:  Parent$Flav by Parent$Tree.ID
## t = -1.141, df = 19.612, p-value = 0.2676
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.31409194  0.09215861
## sample estimates:
## mean in group EC103 mean in group EC201 
##            1.599833            1.710800
## 
##  Welch Two Sample t-test
## 
## data:  Parent$NBI by Parent$Tree.ID
## t = 0.47573, df = 14.7, p-value = 0.6413
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.076667  7.987334
## sample estimates:
## mean in group EC103 mean in group EC201 
##            33.14333            31.68800

Interesting sample pattern here, Chl and NBI start low and work high, Flav does the opposite. Maybe accuracy of measurements?

Parental Cross Analysis (F1 Generation)

A Brief Look at Some Tree Data

## # A tibble: 6 x 5
##   Tree.ID   Chl   NBI   Anth  Flav
##   <fct>   <dbl> <dbl>  <dbl> <dbl>
## 1 IN4BT    34.5  16.2 0.110   2.17
## 2 IN4BV    27.5  13.8 0.121   2.01
## 3 IN4BW    24.8  12.5 0.127   1.99
## 4 IN4BX    37.6  18.4 0.0905  2.05
## 5 IN4BY    24.2  12.5 0.110   1.98
## 6 IN4BZ    32.0  15.6 0.106   2.09

Anthocyanin Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Anth
##              Df Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158 0.5253 0.0033247  1.8093 2.001e-08 ***
## Residuals  1633 3.0007 0.0018376                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Chlorophyll Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Chl
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## F1$Tree.ID  158  47130  298.29  1.3395 0.004474 **
## Residuals  1633 363634  222.68                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Flavonol Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Flav
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158 25.118 0.15897  3.0531 < 2.2e-16 ***
## Residuals  1633 85.030 0.05207                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Nitrogen Overview

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$NBI
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158  16378 103.661  1.5147 8.435e-05 ***
## Residuals  1633 111761  68.439                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

A Closer Look at Invidual F1 Trees and Their Measurements

Msr = group_by(F1, Tree.ID, measure)
Msr = summarise(Msr, Anth = mean(Anth), Flav = mean(Flav), Chl = mean(Chl),NBI = mean(NBI))

#Select Random Column
#sample(1:4,10, replace = T)
#[1] 3 3 2 1 1 4 1 2 4 3

#Select Random Row
#sample(1:50,10, replace = T)
#[1] 34 37 44 20 44 47  9 19 22 40

IN4G5

IN4EP

IN4EY

IN4CD

IN4D8

IN4GV

IN4C2

IN4E3

IN4HK

IN4GC

F1 Height Analysis

## # A tibble: 3 x 5
##   Height   Anth   Chl  Flav   NBI
##   <fct>   <dbl> <dbl> <dbl> <dbl>
## 1 H      0.105   33.9  1.93  17.9
## 2 L      0.0968  35.6  1.98  18.5
## 3 M      0.0913  35.3  1.98  18.1

Height Measure Distributions

Anthocyanin

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorophyll

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Analysis of Variance Table
## 
## Response: F1$Anth
##             Df Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height    2 0.0526 0.0262759  13.533 1.468e-06 ***
## Residuals 1789 3.4735 0.0019416                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height    2    853  426.30  1.8605 0.1559
## Residuals 1789 409911  229.13
## Analysis of Variance Table
## 
## Response: F1$Flav
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height    2   0.915 0.45758  7.4943 0.0005739 ***
## Residuals 1789 109.232 0.06106                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$NBI
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height    2    100  50.044  0.6992 0.4971
## Residuals 1789 128039  71.570

Row Analysis

Sample of Row Means

## # A tibble: 6 x 5
##   Row     Chl   NBI   Anth  Flav
##   <fct> <dbl> <dbl>  <dbl> <dbl>
## 1 1      35.6  17.3 0.107   2.09
## 2 10     30.5  15.7 0.112   1.98
## 3 11     29.9  15.0 0.112   2.04
## 4 12     34.6  18.7 0.0968  1.84
## 5 13     31.3  17.5 0.107   1.86
## 6 14     35.0  17.9 0.0888  1.98

Investigating Differences Between Rows with 2,3 and 4 Trees.

Summary of Rows with 2 Trees

##       Row         Chl             NBI             Anth       
##  17     :1   Min.   :32.13   Min.   :17.14   Min.   :0.1163  
##  0      :0   1st Qu.:32.13   1st Qu.:17.14   1st Qu.:0.1163  
##  1      :0   Median :32.13   Median :17.14   Median :0.1163  
##  10     :0   Mean   :32.13   Mean   :17.14   Mean   :0.1163  
##  11     :0   3rd Qu.:32.13   3rd Qu.:17.14   3rd Qu.:0.1163  
##  12     :0   Max.   :32.13   Max.   :17.14   Max.   :0.1163  
##  (Other):0                                                   
##       Flav      
##  Min.   :1.887  
##  1st Qu.:1.887  
##  Median :1.887  
##  Mean   :1.887  
##  3rd Qu.:1.887  
##  Max.   :1.887  
## 

Summary of Rows with 3 Trees

##       Row          Chl             NBI             Anth        
##  1      : 1   Min.   :29.19   Min.   :14.38   Min.   :0.07868  
##  10     : 1   1st Qu.:32.02   1st Qu.:16.44   1st Qu.:0.08535  
##  11     : 1   Median :34.38   Median :17.81   Median :0.09354  
##  14     : 1   Mean   :34.97   Mean   :18.12   Mean   :0.09677  
##  15     : 1   3rd Qu.:37.05   3rd Qu.:19.46   3rd Qu.:0.10917  
##  16     : 1   Max.   :43.89   Max.   :23.16   Max.   :0.12239  
##  (Other):33                                                    
##       Flav      
##  Min.   :1.820  
##  1st Qu.:1.931  
##  Median :1.974  
##  Mean   :1.973  
##  3rd Qu.:2.037  
##  Max.   :2.092  
## 

Summary of Rows with 4 Trees

##       Row         Chl             NBI             Anth        
##  12     :1   Min.   :31.33   Min.   :16.99   Min.   :0.08183  
##  13     :1   1st Qu.:34.20   1st Qu.:17.74   1st Qu.:0.09326  
##  23     :1   Median :35.08   Median :18.29   Median :0.09747  
##  32     :1   Mean   :35.09   Mean   :18.48   Mean   :0.09702  
##  41     :1   3rd Qu.:36.36   3rd Qu.:19.10   3rd Qu.:0.10265  
##  45     :1   Max.   :38.42   Max.   :20.71   Max.   :0.10721  
##  (Other):4                                                    
##       Flav      
##  Min.   :1.839  
##  1st Qu.:1.898  
##  Median :1.944  
##  Mean   :1.941  
##  3rd Qu.:1.995  
##  Max.   :2.032  
## 

Looking at Row Differences in Variance

## [1] 0.0001562666
## [1] 6.039853e-05
## [1] 14.76679
## [1] 4.10476
## [1] 0.004848629
## [1] 0.004125519
## [1] 5.602987
## [1] 1.155704

T.Tests to Compare Similarity Between Rows with 3 and Rows with 4 Trees.

## 
##  Welch Two Sample t-test
## 
## data:  R4$Anth and R3$Anth
## t = 0.081174, df = 22.551, p-value = 0.936
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.006306855  0.006821440
## sample estimates:
##  mean of x  mean of y 
## 0.09702315 0.09676586
## 
##  Welch Two Sample t-test
## 
## data:  R4$Chl and R3$Chl
## t = 0.13309, df = 27.683, p-value = 0.8951
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.702350  1.938809
## sample estimates:
## mean of x mean of y 
##  35.08876  34.97053
## 
##  Welch Two Sample t-test
## 
## data:  R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08156262  0.01725693
## sample estimates:
## mean of x mean of y 
##  1.941185  1.973338
## 
##  Welch Two Sample t-test
## 
## data:  R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08156262  0.01725693
## sample estimates:
## mean of x mean of y 
##  1.941185  1.973338

There appears to be no signficant differences between Rows with 3 trees and rows with 4 trees for any of the measures.

Row Plots and ANOVAs

Anthocyanin

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Chlorophyll

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Flavonol

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Nitrogen

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Column Analysis

## # A tibble: 4 x 5
##   Column   Chl   NBI   Anth  Flav
##   <fct>  <dbl> <dbl>  <dbl> <dbl>
## 1 1       33.8  17.2 0.0945  2.01
## 2 2       35.4  18.6 0.0992  1.94
## 3 3       36.0  18.7 0.0990  1.96
## 4 4       34.3  18.7 0.0912  1.88
## Analysis of Variance Table
## 
## Response: F1$Anth
##             Df Sum Sq   Mean Sq F value Pr(>F)
## F1$Column    3 0.0122 0.0040655  2.0687 0.1024
## Residuals 1788 3.5138 0.0019652
## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## F1$Column    3   1586  528.66  2.3101 0.07453 .
## Residuals 1788 409178  228.85                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Flav
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## F1$Column    3   2.305 0.76823  12.737 3.087e-08 ***
## Residuals 1788 107.843 0.06031                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$NBI
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## F1$Column    3    821 273.626  3.8427 0.009333 **
## Residuals 1788 127318  71.207                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Variance Table
## 
## Response: F1$Anth
##                              Df  Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height                     2 0.05255 0.0262759 14.9537 3.788e-07 ***
## F1$Column                     3 0.01228 0.0040942  2.3300 0.0727244 .  
## F1$Row                       49 0.23127 0.0047199  2.6861 6.175e-09 ***
## F1$Height:F1$Column           6 0.02055 0.0034247  1.9490 0.0699958 .  
## F1$Height:F1$Row             98 0.20024 0.0020433  1.1628 0.1394606    
## F1$Column:F1$Row            106 0.28473 0.0026861  1.5287 0.0006952 ***
## F1$Height:F1$Column:F1$Row  212 0.41375 0.0019517  1.1107 0.1490621    
## Residuals                  1315 2.31066 0.0017572                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Anth
##                    Df  Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height           2 0.05255 0.0262759 14.2135 7.586e-07 ***
## F1$Row             49 0.23404 0.0047763  2.5837 2.178e-08 ***
## F1$Height:F1$Row   98 0.20394 0.0020810  1.1257    0.1942    
## Residuals        1642 3.03551 0.0018487                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Chl
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height                     2    853  426.30  2.0422  0.130152    
## F1$Column                     3   1577  525.57  2.5178  0.056677 .  
## F1$Row                       49  21813  445.16  2.1326 1.244e-05 ***
## F1$Height:F1$Column           6   1282  213.61  1.0233  0.408244    
## F1$Height:F1$Row             98  27033  275.85  1.3215  0.022708 *  
## F1$Column:F1$Row            106  24037  226.77  1.0863  0.265386    
## F1$Height:F1$Column:F1$Row  212  59670  281.46  1.3484  0.001427 ** 
## Residuals                  1315 274499  208.74                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Chl
##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## F1$Height           2    853  426.30  1.9372  0.14444    
## F1$Row             49  21279  434.27  1.9734 8.34e-05 ***
## F1$Height:F1$Row   98  27286  278.43  1.2652  0.04464 *  
## Residuals        1642 361346  220.06                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Chl
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row      49  21394  436.61  1.9533 0.0001046 ***
## Residuals 1742 389370  223.52                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$Flav
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height                     2  0.915 0.45758  9.3660 9.143e-05 ***
## F1$Column                     3  2.323 0.77447 15.8523 3.957e-10 ***
## F1$Row                       49  8.139 0.16611  3.4001 1.213e-13 ***
## F1$Height:F1$Column           6  1.004 0.16728  3.4240  0.002334 ** 
## F1$Height:F1$Row             98  5.743 0.05860  1.1995  0.096365 .  
## F1$Column:F1$Row            106 14.790 0.13953  2.8559 < 2.2e-16 ***
## F1$Height:F1$Column:F1$Row  212 12.988 0.06126  1.2540  0.012358 *  
## Residuals                  1315 64.245 0.04886                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$NBI
##                              Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height                     2    100  50.044  0.7850 0.4563286    
## F1$Column                     3    818 272.802  4.2793 0.0051365 ** 
## F1$Row                       49   7816 159.511  2.5021 8.551e-08 ***
## F1$Height:F1$Column           6    792 132.004  2.0707 0.0539220 .  
## F1$Height:F1$Row             98   8175  83.418  1.3085 0.0268723 *  
## F1$Column:F1$Row            106   7765  73.250  1.1490 0.1510179    
## F1$Height:F1$Column:F1$Row  212  18842  88.877  1.3942 0.0004396 ***
## Residuals                  1315  83831  63.750                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: F1$NBI
##                    Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row             49   7866 160.527  2.3456 6.383e-07 ***
## F1$Column           3    789 263.021  3.8432   0.00934 ** 
## F1$Row:F1$Column  106   7724  72.864  1.0647   0.31303    
## Residuals        1633 111761  68.439                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Controlling for Height,Column,Row effects

Dat = F1
#Total Mean
Dat$ATmean = mean(Dat$Anth)
Dat$CTmean = mean(Dat$Chl)
Dat$FTmean = mean(Dat$Flav)
Dat$NTmean = mean(Dat$NBI)

#Remove Total Mean
Dat$Anth2 = Dat$Anth - Dat$ATmean
Dat$Chl2 = Dat$Chl - Dat$CTmean
Dat$Flav2 = Dat$Flav - Dat$FTmean
Dat$NBI2 = Dat$NBI - Dat$NTmean

#Height Mean
DatH = group_by(Dat, Height)
DatH = summarise(DatH, HAnth = mean(Anth, na.rm = T), HFlav = mean(Flav,na.rm = T))

#Add Height Mean to Data
Dat =merge(Dat, DatH, by.x = "Height")

#Calculate Height Mean Deviation from Total Mean 
Dat$ATH <- Dat$ATmean - Dat$HAnth
Dat$FTH = Dat$FTmean - Dat$HFlav

#Controlling for Height Anth and Flav
Dat$Anth3 = Dat$Anth2 + Dat$ATH
Dat$Flav3 = Dat$Flav2 + Dat$FTH

#Column Mean
DatC = group_by(Dat, Column)
DatC = summarise(DatC, CFlav = mean(Flav,na.rm = T), CNBI = mean(NBI,na.rm = T))

#Add Height Mean to Data
Dat =merge(Dat, DatC, by.x = "Column")

#Calculate Height Mean Deviation from Total Mean 
Dat$FTH = Dat$FTmean - Dat$CFlav
Dat$NTH = Dat$NTmean - Dat$CNBI

#Controlling for Column Flav and NBI
Dat$Flav4 = Dat$Flav3 + Dat$FTH
Dat$NBI3 = Dat$NBI2 + Dat$NTH

#Row Mean
DatR = group_by(Dat, Row)
DatR = summarise(DatR, RAnth = mean(Anth,na.rm = T),RChl = mean(Chl,na.rm = T), RFlav = mean(Flav,na.rm = T), RNBI = mean(NBI,na.rm = T))

#Add Row Mean to Data
Dat =merge(Dat, DatR, by.x = "Row")

#Calculate Row Mean Deviation from Total Mean 
Dat$ATR = Dat$ATmean - Dat$RAnth
Dat$CTR = Dat$CTmean - Dat$RChl
Dat$FTR = Dat$FTmean - Dat$RFlav
Dat$NTR = Dat$NTmean - Dat$RNBI

#Controlling for Row in Anth, Chl, Flav and NBI
Dat$Anth4 = Dat$Anth3 + Dat$ATR
Dat$Chl3 = Dat$Chl2 + Dat$CTR
Dat$Flav5 = Dat$Flav4 + Dat$FTR
Dat$NBI4 = Dat$NBI3 + Dat$NTR

#Anova Checks
AnAnth = lm(Dat$Anth4 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnAnth)
## Analysis of Variance Table
## 
## Response: Dat$Anth4
##                                 Df  Sum Sq   Mean Sq F value    Pr(>F)    
## Dat$Height                       2 0.00000 0.0000012  0.0007 0.9993236    
## Dat$Column                       3 0.00900 0.0029994  1.7070 0.1637135    
## Dat$Row                         49 0.00061 0.0000125  0.0071 1.0000000    
## Dat$Height:Dat$Column            6 0.02055 0.0034247  1.9490 0.0699958 .  
## Dat$Height:Dat$Row              98 0.20024 0.0020433  1.1628 0.1394606    
## Dat$Column:Dat$Row             106 0.28473 0.0026861  1.5287 0.0006952 ***
## Dat$Height:Dat$Column:Dat$Row  212 0.41375 0.0019517  1.1107 0.1490621    
## Residuals                     1315 2.31066 0.0017572                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AnChl = lm(Dat$Chl3 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnChl)
## Analysis of Variance Table
## 
## Response: Dat$Chl3
##                                 Df Sum Sq Mean Sq F value   Pr(>F)   
## Dat$Height                       2    737  368.65  1.7660 0.171413   
## Dat$Column                       3   1956  652.10  3.1239 0.025073 * 
## Dat$Row                         49    155    3.17  0.0152 1.000000   
## Dat$Height:Dat$Column            6   1282  213.61  1.0233 0.408244   
## Dat$Height:Dat$Row              98  27033  275.85  1.3215 0.022708 * 
## Dat$Column:Dat$Row             106  24037  226.77  1.0863 0.265386   
## Dat$Height:Dat$Column:Dat$Row  212  59670  281.46  1.3484 0.001427 **
## Residuals                     1315 274499  208.74                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AnFlav = lm(Dat$Flav5 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnFlav)
## Analysis of Variance Table
## 
## Response: Dat$Flav5
##                                 Df Sum Sq  Mean Sq F value    Pr(>F)    
## Dat$Height                       2  0.001 0.000491  0.0101  0.989993    
## Dat$Column                       3  0.012 0.004029  0.0825  0.969582    
## Dat$Row                         49  0.215 0.004387  0.0898  1.000000    
## Dat$Height:Dat$Column            6  1.004 0.167281  3.4240  0.002334 ** 
## Dat$Height:Dat$Row              98  5.743 0.058602  1.1995  0.096365 .  
## Dat$Column:Dat$Row             106 14.790 0.139527  2.8559 < 2.2e-16 ***
## Dat$Height:Dat$Column:Dat$Row  212 12.988 0.061264  1.2540  0.012358 *  
## Residuals                     1315 64.245 0.048855                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AnNBI = lm(Dat$NBI4 ~ Dat$Height*Dat$Column*Dat$Row)
anova(AnNBI)
## Analysis of Variance Table
## 
## Response: Dat$NBI4
##                                 Df Sum Sq Mean Sq F value    Pr(>F)    
## Dat$Height                       2     80  39.936  0.6264 0.5346458    
## Dat$Column                       3     50  16.555  0.2597 0.8544529    
## Dat$Row                         49     22   0.451  0.0071 1.0000000    
## Dat$Height:Dat$Column            6    792 132.004  2.0707 0.0539220 .  
## Dat$Height:Dat$Row              98   8175  83.418  1.3085 0.0268723 *  
## Dat$Column:Dat$Row             106   7765  73.250  1.1490 0.1510179    
## Dat$Height:Dat$Column:Dat$Row  212  18842  88.877  1.3942 0.0004396 ***
## Residuals                     1315  83831  63.750                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#New Heatmaps
#DatHM = group_by(Dat, Column, Row)
#DatHM = summarise(DatHM, Anth = mean(Anth4,na.rm = T), Chl = mean(Chl3,na.rm = T), Flav = mean(Flav5,na.rm = T), NBI = mean(NBI4,na.rm = T))
#write.xlsx(as.data.frame(DatHM), file = "DatHM.xlsx", sheetName ="Dat",col.names = T, row.names = F)

#Anthocyanin Heatmaps
AnthDoc <-read.xlsx("Anth.xlsx", sheetName = "Anth")
heatmaply(AnthDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Anth Before Correction")
AnthDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "Anth")
heatmaply(AnthDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Anth After Correction")
#Chlorophyll Heatmaps
ChlDoc<-read.xlsx("Chl.xlsx", sheetName = "Chl")
heatmaply(ChlDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Chl Before Correction")
ChlDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "Chl")
heatmaply(ChlDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Chl After Correction")
#Flavonol Heatmaps
FlavDoc<-read.xlsx("Flav.xlsx", sheetName = "Flav")
heatmaply(FlavDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Flav Before Correction")
FlavDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "Flav")
heatmaply(FlavDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "Flav After Correction")
#NBI Heatmaps
NBIDoc<-read.xlsx("NBI.xlsx", sheetName = "NBI")
heatmaply(NBIDoc, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "NBI Before Correction")
NBIDoc2 <-read.xlsx("DatHM2.xlsx", sheetName = "NBI")
heatmaply(NBIDoc2, xlab = "Column",ylab ="Row", Rowv = FALSE, Colv = FALSE, main = "NBI After Correction")

Looking at controlled data

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Correlations Between Measures

Comparing correlations between the 4 dualex measures

## Analysis of Variance Table
## 
## Response: Anth4
##                   Df  Sum Sq  Mean Sq  F value    Pr(>F)    
## Chl3               1 0.30271 0.302706 192.4807 < 2.2e-16 ***
## Flav5              1 0.05792 0.057917  36.8277 1.572e-09 ***
## NBI4               1 0.01331 0.013306   8.4608 0.0036737 ** 
## Chl3:Flav5         1 0.02196 0.021955  13.9607 0.0001925 ***
## Chl3:NBI4          1 0.03008 0.030085  19.1299 1.292e-05 ***
## Flav5:NBI4         1 0.00791 0.007905   5.0266 0.0250834 *  
## Chl3:Flav5:NBI4    1 0.00005 0.000055   0.0348 0.8519736    
## Residuals       1784 2.80562 0.001573                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: Chl3
##                    Df Sum Sq Mean Sq    F value    Pr(>F)    
## Anth4               1  36383   36383 1.9357e+04 < 2.2e-16 ***
## Flav5               1    383     383 2.0354e+02 < 2.2e-16 ***
## NBI4                1 342014  342014 1.8196e+05 < 2.2e-16 ***
## Anth4:Flav5         1    569     569 3.0285e+02 < 2.2e-16 ***
## Anth4:NBI4          1    115     115 6.1416e+01 7.879e-15 ***
## Flav5:NBI4          1   6552    6552 3.4859e+03 < 2.2e-16 ***
## Anth4:Flav5:NBI4    1      1       1 3.2830e-01    0.5667    
## Residuals        1784   3353       2                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: Flav5
##                   Df Sum Sq Mean Sq   F value    Pr(>F)    
## Anth4              1  2.455   2.455  198.4244 < 2.2e-16 ***
## Chl3               1  0.105   0.105    8.4580  0.003679 ** 
## NBI4               1 73.486  73.486 5940.1653 < 2.2e-16 ***
## Anth4:Chl3         1  0.438   0.438   35.4071 3.213e-09 ***
## Anth4:NBI4         1  0.121   0.121    9.7847  0.001788 ** 
## Chl3:NBI4          1  0.295   0.295   23.8417 1.139e-06 ***
## Anth4:Chl3:NBI4    1  0.028   0.028    2.2925  0.130175    
## Residuals       1784 22.070   0.012                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: NBI4
##                    Df Sum Sq Mean Sq    F value    Pr(>F)    
## Anth4               1  12013   12013 1.5967e+04 < 2.2e-16 ***
## Chl3                1  95398   95398 1.2679e+05 < 2.2e-16 ***
## Flav5               1   9255    9255 1.2300e+04 < 2.2e-16 ***
## Anth4:Chl3          1     36      36 4.7348e+01 8.199e-12 ***
## Anth4:Flav5         1    118     118 1.5676e+02 < 2.2e-16 ***
## Chl3:Flav5          1   1393    1393 1.8521e+03 < 2.2e-16 ***
## Anth4:Chl3:Flav5    1      1       1 1.6591e+00    0.1979    
## Residuals        1784   1342       1                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Flowering

## Analysis of Variance Table
## 
## Response: Dat$Anth4
##              Df Sum Sq    Mean Sq F value Pr(>F)
## Dat$Flower    1 0.0002 0.00023977  0.1325 0.7159
## Residuals  1790 3.2393 0.00180967
## Analysis of Variance Table
## 
## Response: Dat$Chl3
##              Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower    1     37  36.997  0.1701 0.6801
## Residuals  1790 389333 217.505
## Analysis of Variance Table
## 
## Response: Dat$Flav5
##              Df Sum Sq  Mean Sq F value Pr(>F)  
## Dat$Flower    1  0.248 0.247514  4.4866 0.0343 *
## Residuals  1790 98.750 0.055168                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
## 
## Response: Dat$NBI4
##              Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower    1     12  11.938  0.1788 0.6725
## Residuals  1790 119544  66.784

Looking at Environment and Height